TY - JOUR
T1 - Preface to the special issue on engineering of computer-based systems
AU - Kofroň, Jan
AU - Margaria, Tiziana
AU - Seceleanu, Cristina
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.
PY - 2025/2
Y1 - 2025/2
N2 - This special issue contains nine extended and rigorously peer-reviewed papers selected from those originally presented at ECBS 2023, the 8th International Conference on Engineering of Computer-Based Systems, held at Mälardalen University, Sweden, October 16-18, 2023, under the theme “Engineering for Responsible AI”. The included papers represent innovative contributions addressing critical aspects of responsible artificial intelligence and integrated engineering practices. These contributions span from formal verification and security analyses of IoT protocols and federated learning frameworks to machine learning-based simulations and predictions in hardware and software systems. The selection also includes work on automata learning techniques for protocol compliance, continuous integration approaches for neural network-based autonomous systems, assertion usage in software testing, language-driven engineering for code generation, and the integration of IoT backends in digital twin infrastructures. Together, these papers showcase recent advances, offering valuable insights into the rigorous integration of modern technologies within complex, computer-based systems.
AB - This special issue contains nine extended and rigorously peer-reviewed papers selected from those originally presented at ECBS 2023, the 8th International Conference on Engineering of Computer-Based Systems, held at Mälardalen University, Sweden, October 16-18, 2023, under the theme “Engineering for Responsible AI”. The included papers represent innovative contributions addressing critical aspects of responsible artificial intelligence and integrated engineering practices. These contributions span from formal verification and security analyses of IoT protocols and federated learning frameworks to machine learning-based simulations and predictions in hardware and software systems. The selection also includes work on automata learning techniques for protocol compliance, continuous integration approaches for neural network-based autonomous systems, assertion usage in software testing, language-driven engineering for code generation, and the integration of IoT backends in digital twin infrastructures. Together, these papers showcase recent advances, offering valuable insights into the rigorous integration of modern technologies within complex, computer-based systems.
KW - Automata learning
KW - Digital twins
KW - Machine learning
KW - Security of IoT
KW - Testing
UR - https://www.scopus.com/pages/publications/105005269510
U2 - 10.1007/s10009-025-00807-z
DO - 10.1007/s10009-025-00807-z
M3 - Article
AN - SCOPUS:105005269510
SN - 1433-2779
VL - 27
SP - 1
EP - 3
JO - International Journal on Software Tools for Technology Transfer
JF - International Journal on Software Tools for Technology Transfer
IS - 1
ER -